Another family of AD methods are based on generative approaches that learn to reconstruct input features, and use the error of reconstructions or density to identify anomalies.
[1]} employs auto-encoders for image data.
DAGMM [2]} first learns an auto-encoder and uses a Gaussian Mixture Model to estimate density in the low-dimensional latent space.
Since these generative approaches are based on reconstructing input features, they may not easily fit in high-dimensional tabular data with noisy and heterogeneous features and small amount of labeled examples.
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